Below, I grab Investigatory Stop Report (ISR) data from the Chicago Police Department's website which is available for stops over time periods January 1, 2016 to January 16, 2018 and January 1, 2018 to December 31, 2018. I then deduplicate records and do some data type conversions to prepare for the visualizations.
Note: I load data from the web and clean all within this file.
I begin with a basic line chart that demonstrates the total ISRs by month over time. Seasonal dips in the number of stops per month occur towards the end of fall and tend to increase again in late winter/early spring. There is a noticeable increase in the volume of stops during the high season of 2018 as compared to the prior two years. In later charts, I continue to examine trends over time to see if there are patterns which may explain this increase.
After observing how stops have fluctuated over time, I want to examine whether these trends demonstrate any differences by police district. Below, it is apparent that District 7 likely made up the lion's share of the spike in ISRs in mid-2016. The subsequent spike in stops in mid-2018 occurred in multiple police districts but appears particularly strong in districts 11 and 7.
In the city of Chicago, district 11 is located on the west side and primarily encompasses West Garfield Park, a neighborhood notorious for high rates of homicide. District 7 primarily encompasses Englewood on the south side. The populations in both districts are predominantly African American. Below, I map ISRs in the year 2018 by police district to provide geographical context.
Note: I have every intention of mapping the specific location of stops but I only just got access to a Midway account at RCC for geocoding.
Racially discriminatory policing is a pernicious issue in police departments across the country and Chicago is no exception. In the stack area chart below, I used color and area to visualize how the stops have varied by race over time. CPD has consistently stopped Black/African American individuals at the highest rate and by a wide margin, followed by Hispanic.
Note: Black and White Hispanic are combined into "Hispanic". "Other, Unknown" includes Asian/Pacific Islanders and American Indian/Alaska Natives.
The ISR data provided by the city of Chicago includes an indicator for whether or not the stop included a pat down and whether or not those pat downs included a search. Below, I explore how pat downs and searches have varied over time. We see that pat downs and searches have varied in conjunction with ISRs overall over time. However, most of the rise in ISRs in 2018 were driven by ISRs that did not involve pat downs or searches.
Given findings from the previous two visualizations, one might wonder if the relative likelihood of being patted down or searched after a stop differs by race. I have found that from 2016-2018, the chances of a Black/African American or Hispanic subjects being patted down or searched after a stop has hovered at close to 5%. However, for white subjects, the chances of being patted down or searched after a stop has not exceeded 4% since the early months of 2016.
Many policy advocates have touted the use of body cameras as a mechanism to prevent police brutality. Below, I explore the volume of ISRs that have been recorded by a body camera. The use of body cameras has noticeable increased over the past couple years. The rate of adoption has varied by district but increased across all of them.
The adoption of body cameras has not resulted in a reduction of the volume of stops overall and stops have continued to disproportionately target people of color, as seen in previous charts.
The next topic I will explore is the relationship between ISRs and crime. To do so, I take data from the city of Chicago's data portal.
Below, I repeat the monthly line chart that I created for ISRs above, but I add in a new line demonstrating crimes and distinguish the two using color. There is some correlation between the volume of ISRs and crimes over time. However, throughout the year, it seems ISRs begin pre-emptively increase around December/January whereas crime does not begin its uptick until later in the year in February/March.
Below, I replicate the line chart above but this time I facet each chart by district. In this way, I use a text encoding to denote which chart represents each district. Here, we see some disparities and variation over time and by district with regards to the relationship between crimes and ISRs. For example, in district 11, ISRs suddenly drop in 2017 without a corresponding drop in crime. In district 1, ISRs suddenly increase in 2018 without a corresponding increas in crime. Districts 17 and 18 appear to have relatively similar levels of ISRs over time despite district 18 having consistently higher levels of crime.
Below, I map police districts to provide visual context for where there is a discrepancy between crimes and ISRs. In 2018, crimes were highly concentrated in the loop and the Near North Side neighborhoods which roughly corresponds to police districts 1 and 18. We see in the map below that all lakefront districts near downtown had above average crime rates but below average ISRs compared to the rest of the city.
*Chicago Police Department's Investigatory Stop Reports data can be found at https://home.chicagopolice.org/statistics-data/isr-data/ and https://home.chicagopolice.org/isr-data-2018/
^The crimes dataset on Chicago's Data Portal can be found at https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2/data